Applied Linear Regression (Wiley Series in Probability and Statistics) [Hardcover]

Sanford Weisberg (Author)

Book Description

ISBN-10: 0471879576 | ISBN-13: 978-0471879572 | Publication Date: July 1985 | Edition: 2

Nonlinear Statistical Methods A. Ronald Gallant Describes the recent advances in statistical and probability theory that have removed obstacles to an adequate theory of estimation and inference for nonlinear models. Thoroughly explains theory, methods, computations, and applications. Covers the three major categories of statistical models that relate dependent variables to explanatory variables: univariate regression models, multivariate regression models, and simultaneous equations models. Includes many figures which illustrate computations with SAS(R) code and resulting output. 1987 (0 471-80260-3) 610 pp. Exploring Data Tables, Trends, and Shapes Edited by David C. Hoaglin, Frederick Mosteller, and John W. Tukey Together with its companion volume, Understanding Robust and Exploratory Data Analysis, this work provides a definitive account of exploratory and robust/resistant statistics. It presents a variety of more advanced techniques and extensions of basic exploratory tools, explains why these further developments are valuable, and provides insight into how and why they were invented. In addition to illustrating these techniques, the book traces aspects of their development from classical statistical theory. 1985 (0 471-09776-4) 672 pp. Robust Regression & Outlier Detection Peter J. Rousseeuw and Annick M. Leroy An introduction to robust statistical techniques that have been developed to isolate or identify outliers. Emphasizes simple, intuitive ideas and their application in actual use. No prior knowledge of the field is required. Discusses robustness in regression, simple regression, robust multiple regression, the special case of one-dimensional location, and outlier diagnostics. Also presents an outlook of robustness in related fields such as time series analysis. Emphasizes "high-breakdown" methods that can cope with a sizable fraction of contamination. Focuses on the least median of squares method, which appeals to the intuition and is easy to use. 1987 (0 471-85233-3) 329 pp.

Editorial Reviews

Review

“…this is an excellent book which could easily be used as a course text…” (International Statistical Institute, January 2006)

"Twenty years after the release of the excellent previous edition, the author has succeeded in putting together a superb and inviting third edition…" (Technometrics, August 2005) --This text refers to an alternate Hardcover edition.

From the Inside Flap

Applied Linear Regression, Second Edition is a comprehensive guide to the methods of applied linear regression. Focusing on model building, assessing fit and reliability, and drawing conclusions, it develops estimation, confidence, and testing procedures mostly using least squares. Throughout, the importance of assumptions and their relevance in specific problems is stressed. Updated to reflect the enormous progress in the area of linear regression since the First Edition in 1980, the Second Edition cites more than 60 references, and includes several new problems, figures, and a totally new chapter that introduces students to nonlinear, logistic, and generalized linear regression models. Containing more than 20 worked examples, real data is used to illustrate variable selection, new predictor construction and dummy variables, model validation and other topics. Applied Linear Regression, Second Edition provides the most in-depth coverage available on transforming variables, finding problems with assumptions, and identifying influential cases. It discusses the special problems of inference and prediction from regression models. And throughout, graphical methods are generously discussed and illustrated. Additional topics include:

• Standard results for simple and multiple regression.
• The difficulties of using and interpreting regression models and estimates.
• Model building, variable selection, adding polynomials, and choosing transformations.
• Regression diagnostics, assumptions, and influence of cases.

See all Editorial Reviews

Product Details

• Hardcover: 344 pages
• Publisher: Wiley; 2 edition (July 1985)
• Language: English
• ISBN-10: 0471879576
• ISBN-13: 978-0471879572
• Product Dimensions: 9.3 x 6.3 x 0.9 inches
• Shipping Weight: 1.4 pounds
• Average Customer Review: 2.0 out of 5 stars  See all reviews (8 customer reviews)
• Amazon Best Sellers Rank: #2,279,954 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

8 of 10 people found the following review helpful:

4.0 out of 5 stars Good overview, May 4, 2000

By 

Roger Peng (Baltimore, MD USA) - See all my reviews
(REAL NAME)   

This review is from: Applied Linear Regression (Wiley Series in Probability and Statistics) (Hardcover)

Weisberg's book is a good introduction/overview of the principal techniques used in linear models. However, in many situations, he leaves out many details and derivations and either directs the reader to the references or says nothing. I would have liked it if he had put in a few more mathematical derivations and had been more thorough in the discussion about collinearity and variable selection.

10 of 15 people found the following review helpful:

5.0 out of 5 stars An excellent introduction to linear regression, April 4, 2000

By A Customer

This review is from: Applied Linear Regression (Wiley Series in Probability and Statistics) (Hardcover)

This book provides an excellent introduction to the application of linear regression models. Starting with the idea of studying "relationships between measurable variables", it develops the concepts behind simple and multiple linear regression, discusses interpretations of the output, proscribes diagnostics, discusses model building techniques, and touches other topics. It covers most of the material contained in Draper & Smith's _Applied Regression Analysis_, but in half the number of pages.

I would wholeheartedly endorse this book as a primary or supplemental text to an introductory course, or as a text for someone who wants to learn about linear regression on their own.

1 of 2 people found the following review helpful:

1.0 out of 5 stars Overly heuristic, filled with errors, November 13, 2010

By 

griffin "Griffin" - See all my reviews

This review is from: Applied Linear Regression (Wiley Series in Probability and Statistics) (Hardcover)

An absolutely terrible text. The book rarely offers anything remotely resembling rigorous derivations (almost entirely heuristic arguments), and as often as not has errors in the derivations they do present. On top of that, the R package written for this text by the author (package "alr3") contains errors as well. In order to complete the exercises provided in the text, you actually have to EDIT the provided functions.

Overall, this is an inexcusably bad book.

If it's required for a class, see if you can get it from the library, and buy Applied Linear Statistical Models for reference instead. The notation is a little different, but the notational issues are a small price to pay for actually knowing what's going on.